Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms

IF 5.7 3区 环境科学与生态学 Q1 WATER RESOURCES
Sobhan Maleky, Maryam Faraji, Majid Hashemi, Akbar Esfandyari
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引用次数: 0

Abstract

Assessing water quality is essential for acquiring a better understanding of the importance of water in human society. In this study, the quality of groundwater resources in Jiroft city, Iran, using artificial intelligence methods to estimate the groundwater quality index (GWQI) was evaluated. The analysis of hydrochemical parameters, including arsenic (As), fluoride (F), nitrate (NO3), and nitrite (NO2), in 408 samples revealed that concentrations of F, NO3, and NO2 were below the WHO standard threshold, but levels of As exceeded the permissible value. The random forest model with the highest accuracy (R2 = 0.986) was the best prediction model, while logistic regression (R2 = 0.98), decision tree (R2 = 0.979), K-nearest neighbor (R2 = 0.968), artificial neural network (R2 = 0.955), and support vector machine (R2 = 0.928) predicted GWQI with lower accuracy. The non-carcinogenic risk assessment revealed that children had the highest hazard quotient for oral and dermal intake, with values ranging from 0.47 to 13.53 for oral intake and 0.001 to 0.05 for dermal intake. The excess lifetime cancer risk of arsenic for children, adult females, and males was found to be from 2.5 × 10–4 to 7.2 × 10–3, 1.2 × 10–4 to 3.6 × 10–3, and 4.3 × 10–5 to 1.2 × 10–3, respectively. This study suggests that any effort to reduce the arsenic levels in the Jiroft population should take into account the health hazards associated with exposure to arsenic through drinking water.

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来源期刊
Applied Water Science
Applied Water Science WATER RESOURCES-
CiteScore
9.90
自引率
3.60%
发文量
268
审稿时长
13 weeks
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